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Starts 3 June 2025 08:17

Ends 3 June 2025

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Fraud Models in the Energy Sector - Using AI for Smart Meter Theft Detection

Explore AI-driven fraud detection for smart meters in the energy sector, focusing on energy theft prevention and consumption monitoring techniques.
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Data Science Festival

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Overview

Explore AI-driven fraud detection for smart meters in the energy sector, focusing on energy theft prevention and consumption monitoring techniques.

Syllabus

  • Introduction to Fraud Detection in the Energy Sector
  • Overview of the energy sector’s landscape
    Common types of fraud and their impact
    Importance of fraud detection for smart meters
  • Fundamentals of Artificial Intelligence
  • Basics of machine learning and AI
    Key algorithms and techniques: classification, clustering, anomaly detection
    Introduction to neural networks and deep learning
  • Smart Meters and Data Collection
  • Structure and functionality of smart meters
    Types of data collected by smart meters
    Data privacy and ethical considerations
  • Energy Theft Detection Techniques
  • Identifying patterns indicative of energy theft
    Common algorithms used for theft detection
    Case studies of successful detection implementations
  • AI Models for Fraud Detection
  • Anomaly detection models: autoencoders, isolation forests
    Supervised vs. unsupervised learning
    Feature engineering specific to energy consumption data
  • Implementing AI-driven Solutions
  • Data preprocessing and cleansing techniques
    Model training and evaluation
    Handling imbalanced datasets
  • Tools and Technologies
  • Introduction to popular tools: TensorFlow, PyTorch, Scikit-learn
    Leveraging cloud platforms for large-scale data processing and model deployment
    Infrastructure considerations for real-time fraud detection
  • Monitoring and Maintenance of AI Systems
  • Ensuring model robustness and accuracy over time
    Adapting models to evolving patterns of fraud
    Designing human-in-the-loop feedback systems
  • Case Studies and Industry Applications
  • Analysis of recent case studies in energy theft detection
    Lessons learned from deployment and scaling of AI systems in the sector
    Future trends in AI and fraud detection in the energy industry
  • Capstone Project
  • Design and implement a prototype AI system for smart meter theft detection
    Presentation and critique of project findings and methodologies

Subjects

Data Science